Production AI inside FedRAMP-authorized environments. Air-gapped model deployments for classified workloads. NIST AI RMF alignment from the first sprint. CMMC Phase 2 ready. GSA MAS contracted.
Commercial AI tools work until the data touches a federal boundary. Model training on CUI. Inference logs stored outside the authorization boundary. A vendor's API calling home from inside a classified network. The AI pilot that worked in the sandbox fails the ATO review.
Federal AI implementation requires architecture designed for the boundary from the first commit — not patched for compliance after the fact. FedRAMP 20x is rewriting the authorization timeline. CMMC Phase 2 enforcement begins November 10, 2026. Every DIB contractor running AI workflows needs to know where CUI flows before that date.
Federal AI sits at the intersection of cloud authorization, data handling, and AI governance. Every engagement maps to the specific frameworks in scope for your program.
The 2025 authorization framework prioritizes AI/ML services and replaces the legacy Rev 5 control baseline for cloud-native workloads. We design AI architectures inside authorized boundaries — AWS GovCloud, Azure Government, and emerging 20x-authorized services — with an ATO-ready documentation package from the Design stage forward.
Full enforcement on all DoD contracts begins November 10, 2026. Defense contractors running AI workflows that touch CUI need a compliant data flow, a scoped deployment architecture, and documented evidence before their next contract assessment. We scope, design, and document the AI component of CMMC compliance concurrently with the build.
The AI Risk Management Framework (NIST AI 100-1) is the federal standard for AI governance. Our AI implementations produce a mapped RMF profile: Govern (policies, roles, accountability), Map (risk identification), Measure (evaluation metrics), and Manage (response and recovery). Required for civilian agency AI and increasingly referenced by DoD programs.
The right architecture depends on your classification level, data handling requirements, and existing infrastructure. We design for the boundary, not around it.
For unclassified federal workloads where FedRAMP-authorized commercial AI APIs are in scope. AWS Bedrock in GovCloud, Azure OpenAI Service in Azure Government, or equivalent FedRAMP-authorized services. Data stays inside the authorization boundary. Inference logs, retention, and access controls configured to ATO requirements. Fastest time to production for non-CUI use cases.
For CUI workloads where a shared API model isn't acceptable. Open-weight models deployed on dedicated compute inside an authorized VPC. No shared inference infrastructure. Model weights, prompt logs, and outputs stay inside your boundary. Supports CMMC Level 2 and Level 3 data handling requirements. Llama 3, Mistral, and equivalents. Quantized for cost-efficient dedicated GPU instances.
For classified or sensitive compartmented workloads with no external network connectivity. Full model deployment on on-premises hardware. No internet dependency for inference. Weights loaded from verified media. Audit log written to local storage. We design, deploy, and document the model configuration, fine-tuning pipeline, and inference stack for your specific enclave hardware. Requires cleared engineering staff — available through our IT Staffing practice.
Production AI use cases across civilian agencies and defense contractors. Each one has a compliance constraint that determines the architecture.
Large-volume document processing, extraction, and classification inside the authorization boundary. Legal filings, contract analysis, acquisition documents, regulatory submissions. RAG pipelines over agency knowledge bases without data leaving the enclave.
AI-assisted routing, triage, and draft generation for high-volume operational workflows. Forms processing, correspondence automation, benefits determination support. Deployed inside existing agency platforms with full audit trail and human-in-the-loop controls per NIST AI RMF requirements.
Natural language query layers over federal data platforms. Analysts query their data lake in plain language, with responses grounded in the authorized data sources. No hallucination risk on structured data — retrieval, not generation. Built on existing Snowflake, Databricks, or Fabric environments inside the authorization boundary.
Context from the current federal AI environment. These are not projections. They are the constraints every agency AI program is navigating now.
CMMC Phase 2 full enforcement means every defense contractor handling CUI must have a compliant environment assessed by a C3PAO before renewing or winning DoD contracts. Most contractors know the infrastructure scope. Many have not assessed whether their AI tools — co-pilots, language models, document processors, analytics platforms — are also handling CUI and therefore in scope.
The answer is rarely obvious. A document AI tool that processes acquisition-sensitive contracts is in scope. An analytics co-pilot that queries a CUI data lake is in scope. We run a scoping exercise that maps your AI deployments against CMMC Level 2 and Level 3 practice areas, identifies gaps, and produces the documentation and remediation plan your assessor will need.
CMMC assessments typically take 6–12 months from scoping to C3PAO assessment completion. Contracts signed after November 10, 2026 require a valid assessment. Start the scoping exercise now.
Ingress IT Services holds GSA Multiple Award Schedule contract #47QTCA26D000K. Federal agencies can place task orders directly without a separate competitive procurement — which means faster time from requirement to engagement start.
The schedule covers AI implementation, cloud engineering, data analytics, and IT staffing. If your program requires a specific SIN or labor category confirmation, we provide that documentation on request before the task order is placed.
Answers to what federal program managers and contracting officers typically ask. Longer answers come in the diagnostic call.
We work with federal program managers, contracting officers, and agency CIOs on AI architectures that survive the ATO review. Every engagement starts with a Diagnose stage that scopes your compliance constraints before anything gets built.